This invention relates generally to network-based information analytics and optimization processes, and more particularly to collaborative networking optimized with quality assessment of inter-domain information provided by a network community.
Collaborative networking applications that are enabled through technologies such as Web 2.0 have brought forth the concept of crowd sourcing (also referred to as “the wisdom of crowds”) to several e-business and social networking sites. Web 2.0 refers to an increasingly popular type of web application that is primarily distinguished in its ability to enable network users to contribute information for collaboration and sharing. Common collaborative networking applications include, e.g., social software, web syndication, weblogs, and wikis, to name a few. The ability to tap into the wisdom of the crowds through these applications can be a great differentiating asset for an individual or organization that utilizes these applications. For example, content reviews provided by a large online community can be exploited to determine trends, forecasts, and similar data, thereby enabling a content service provider to implement various monetization strategies derived from this collective wisdom.
The problem is that not every opinion received should be considered equal in terms of the expertise/reputation of those who contribute these opinions. Hence, certain network users who provide opinions on a subject (e.g., a product/service) may not be equally as qualified to comment on the future value of the subject as other network users. The future value of a product/service may be a measure of its success using metrics such as return on investment (ROI) or revenue generated.
It is oftentimes possible to identify a network user's expertise level a-priori based on a subjective evaluation of the network user and/or his/her credentials in a single domain or subject area. For example, a researcher in the field of machine learning may leverage his publishing record in this domain as a credential. However, the value of this researcher's credentials may be more difficult to assess when this individual opines on a subject that is in another domain, such as graphics or operating systems. Given that different individuals have differing expertise and credentials in different domains or areas of expertise, the difficulty in accurately evaluating various opinions of these individuals becomes much more complex. Currently, in today's Web 2.0 network environment, whereby individuals are likely to seamlessly cross domains and prolifically opine on subjects spread out through the domains, the ability to evaluate the opinions of these individuals becomes increasingly burdensome.
There is a need for an objective method for evaluating opinions or predictions gathered over a network from different entities across multiple domains and efficiently using these opinions or predictions to predict a future event (e.g., the success/failure of a product, movie, or a winner in elections, sports tournaments, etc), as well as infer reputation credentials of individuals for a domain according to the performance history of the individuals predictions or opinions in other closely related domains.